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Defined 2 new typedefs one for R datastructures and one for internal datastructures that use floats.
performance improvement attempt for huge datasets
- reduced iterations of kmeans to 4 - kmeans++ seeding centers now done according to efficient weighted sampling - mt19937 random() - uniform_real_distribution 0.01,0.99: Avoid bias towards high random numbers to bias towards high-acceptance range [DEV] kmeans centroids initialization - Low memory/ batch by cluster operation - Verbose for logging progress [FIX] remove existing centroids from kmeans++ init centroids [BUG] replace fill::arma::randu with stdlib::rand() Rcpp armadillo's rand function does not work with randu [FIX] add elements to set during centroid initialization If element exists already then backtrack and retry
Parameterize the batch proportion cutoff
unshuffle and then return
- also print some messages
- When covariate has one trivial level after subsetting it is dropped altogether
Flat dense matrix, not the most memory efficient but better performance
These is just for archival purposes
A cell may belong to several different batches when different covariates exist. The design was assuming that all cells MUST have covariates+1 entries in Phi. However, if for a cell only one batch was dropped but the other covariate has support, this is not true.
- Reproducible clusters by the R set.seed for the same embedding set
…us-integration
- null hypothesis gives a ratio close to 1 - Added pseudocount for cases when E is close to 1
RcppArmadillo/#438
- Fixed vignettes to interface correctly with the new API change - New Getter that gets all lambdas for each cluster
added times
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